def ppo_pendulum(ctxt=None, seed=1):
"""Train PPO with InvertedDoublePendulum-v2 environment.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
"""
set_seed(seed)
env = MetaRLEnv(env_name='InvertedDoublePendulum-v2')
runner = LocalRunner(ctxt)
policy = GaussianMLPPolicy(env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
algo = PPO(env_spec=env.spec,
policy=policy,
value_function=value_function,
max_path_length=100,
discount=0.99,
center_adv=False)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=10000)
def test_maml_trpo_pendulum():
"""Test PPO with Pendulum environment."""
env = MetaRLEnv(normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32))
rollouts_per_task = 5
max_path_length = 100
runner = LocalRunner(snapshot_config)
algo = MAMLTRPO(env=env,
policy=policy,
value_function=value_function,
max_path_length=max_path_length,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=5,
batch_size=rollouts_per_task * max_path_length)
assert last_avg_ret > -5
env.close()
def test_maml_trpo_dummy_named_env():
"""Test with dummy environment that has env_name."""
env = MetaRLEnv(
normalize(DummyMultiTaskBoxEnv(), expected_action_scale=10.))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32))
rollouts_per_task = 2
max_path_length = 100
runner = LocalRunner(snapshot_config)
algo = MAMLTRPO(env=env,
policy=policy,
value_function=value_function,
max_path_length=max_path_length,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
runner.setup(algo, env)
runner.train(n_epochs=2, batch_size=rollouts_per_task * max_path_length)
def maml_trpo_metaworld_ml10(ctxt, seed, epochs, rollouts_per_task,
meta_batch_size):
"""Set up environment and algorithm and run the task.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
epochs (int): Number of training epochs.
rollouts_per_task (int): Number of rollouts per epoch per task
for training.
meta_batch_size (int): Number of tasks sampled per batch.
"""
set_seed(seed)
env = MetaRLEnv(
normalize(mwb.ML10.get_train_tasks(), expected_action_scale=10.))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(100, 100),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
max_path_length = 100
test_task_names = mwb.ML10.get_test_tasks().all_task_names
test_tasks = [
MetaRLEnv(
normalize(mwb.ML10.from_task(task), expected_action_scale=10.))
for task in test_task_names
]
test_sampler = EnvPoolSampler(test_tasks)
meta_evaluator = MetaEvaluator(test_task_sampler=test_sampler,
max_path_length=max_path_length,
n_test_tasks=len(test_task_names))
runner = LocalRunner(ctxt)
algo = MAMLTRPO(env=env,
policy=policy,
value_function=value_function,
max_path_length=max_path_length,
meta_batch_size=meta_batch_size,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1,
meta_evaluator=meta_evaluator)
runner.setup(algo, env)
runner.train(n_epochs=epochs,
batch_size=rollouts_per_task * max_path_length)
def setup_method(self):
"""Setup method which is called before every test."""
self.env = MetaRLEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.value_function = GaussianMLPValueFunction(env_spec=self.env.spec)
def maml_vpg_half_cheetah_dir(ctxt, seed, epochs, rollouts_per_task,
meta_batch_size):
"""Set up environment and algorithm and run the task.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
epochs (int): Number of training epochs.
rollouts_per_task (int): Number of rollouts per epoch per task
for training.
meta_batch_size (int): Number of tasks sampled per batch.
"""
set_seed(seed)
env = MetaRLEnv(normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
max_path_length = 100
task_sampler = SetTaskSampler(lambda: MetaRLEnv(
normalize(HalfCheetahDirEnv(), expected_action_scale=10.)))
meta_evaluator = MetaEvaluator(test_task_sampler=task_sampler,
max_path_length=max_path_length,
n_test_tasks=1,
n_test_rollouts=10)
runner = LocalRunner(ctxt)
algo = MAMLVPG(env=env,
policy=policy,
value_function=value_function,
max_path_length=max_path_length,
meta_batch_size=meta_batch_size,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1,
meta_evaluator=meta_evaluator)
runner.setup(algo, env)
runner.train(n_epochs=epochs,
batch_size=rollouts_per_task * max_path_length)
def setup_method(self):
"""Setup method which is called before every test."""
self.env = MetaRLEnv(
normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.value_function = GaussianMLPValueFunction(env_spec=self.env.spec,
hidden_sizes=(32, 32))
def ppo_metarl_pytorch(ctxt, env_id, seed):
"""Create metarl PyTorch PPO model and training.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
runner = LocalRunner(ctxt)
env = MetaRLEnv(normalize(gym.make(env_id)))
policy = PyTorch_GMP(env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
policy_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
policy,
max_optimization_epochs=10,
minibatch_size=64)
vf_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
value_function,
max_optimization_epochs=10,
minibatch_size=64)
algo = PyTorch_PPO(env_spec=env.spec,
policy=policy,
value_function=value_function,
policy_optimizer=policy_optimizer,
vf_optimizer=vf_optimizer,
max_path_length=hyper_parameters['max_path_length'],
discount=0.99,
gae_lambda=0.95,
center_adv=True,
lr_clip_range=0.2)
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
def mtppo_metaworld_mt10(ctxt, seed, epochs, batch_size, n_worker):
"""Set up environment and algorithm and run the task.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
epochs (int): Number of training epochs.
batch_size (int): Number of environment steps in one batch.
n_worker (int): The number of workers the sampler should use.
"""
set_seed(seed)
tasks = mwb.MT10.get_train_tasks().all_task_names
envs = []
for task in tasks:
envs.append(normalize(MetaRLEnv(mwb.MT10.from_task(task))))
env = MultiEnvWrapper(envs,
sample_strategy=round_robin_strategy,
mode='vanilla')
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
algo = PPO(env_spec=env.spec,
policy=policy,
value_function=value_function,
max_path_length=128,
discount=0.99,
gae_lambda=0.95,
center_adv=True,
lr_clip_range=0.2)
runner = LocalRunner(ctxt)
runner.setup(algo, env, n_workers=n_worker)
runner.train(n_epochs=epochs, batch_size=batch_size)
def setup_method(self):
"""Setup method which is called before every test."""
self._env = MetaRLEnv(gym.make('InvertedDoublePendulum-v2'))
self._runner = LocalRunner(snapshot_config)
self._policy = GaussianMLPPolicy(env_spec=self._env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
self._params = {
'env_spec': self._env.spec,
'policy': self._policy,
'value_function':
GaussianMLPValueFunction(env_spec=self._env.spec),
'max_path_length': 100,
'discount': 0.99,
}
def trpo_pendulum_ray_sampler(ctxt=None, seed=1):
"""Set up environment and algorithm and run the task.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
"""
# Since this is an example, we are running ray in a reduced state.
# One can comment this line out in order to run ray at full capacity
ray.init(memory=52428800,
object_store_memory=78643200,
ignore_reinit_error=True,
log_to_driver=False,
include_webui=False)
deterministic.set_seed(seed)
env = MetaRLEnv(env_name='InvertedDoublePendulum-v2')
runner = LocalRunner(ctxt)
policy = GaussianMLPPolicy(env.spec,
hidden_sizes=[32, 32],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
algo = TRPO(env_spec=env.spec,
policy=policy,
value_function=value_function,
max_path_length=100,
discount=0.99,
center_adv=False)
runner.setup(algo, env, sampler_cls=RaySampler)
runner.train(n_epochs=100, batch_size=1024)
def setup_method(self):
"""Setup method which is called before every test."""
self.env = MetaRLEnv(
normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.value_function = GaussianMLPValueFunction(env_spec=self.env.spec,
hidden_sizes=(32, 32))
self.algo = MAMLPPO(env=self.env,
policy=self.policy,
value_function=self.value_function,
max_path_length=100,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
def mtppo_metaworld_ml1_push(ctxt, seed, epochs, batch_size):
"""Set up environment and algorithm and run the task.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
epochs (int): Number of training epochs.
batch_size (int): Number of environment steps in one batch.
"""
set_seed(seed)
env = MetaRLEnv(normalize(mwb.ML1.get_train_tasks('push-v1')))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
algo = PPO(env_spec=env.spec,
policy=policy,
value_function=value_function,
max_path_length=128,
discount=0.99,
gae_lambda=0.95,
center_adv=True,
lr_clip_range=0.2)
runner = LocalRunner(ctxt)
runner.setup(algo, env)
runner.train(n_epochs=epochs, batch_size=batch_size)
def trpo_metarl_pytorch(ctxt, env_id, seed):
"""Create metarl PyTorch TRPO model and training.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
runner = LocalRunner(ctxt)
env = MetaRLEnv(normalize(gym.make(env_id)))
policy = PyTorch_GMP(env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
algo = PyTorch_TRPO(env_spec=env.spec,
policy=policy,
value_function=value_function,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
gae_lambda=hyper_parameters['gae_lambda'])
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])